The Deconvolution Method for Obtaining Correspondence in Data-Independent Acquisition Mass Spectrometry Data

Although data-independent acquisition (DIA) shows powerful potential in achieving comprehensive peptide information acquisition, the difficulty in determining the precursor m/z and distinguishing fragment ions has posed challenges in DIA data analysis. To address this challenge, a common approach is to recover the correspondence between precursor ions and fragment ions, followed by peptide identification using traditional data-dependent acquisition (DDA) database searching. In this study, we propose a cosine similarity-based deconvolution method that rapidly establishes the correspondence between chromatographic profiles of precursor ions and fragment ions through matrix calculations. Experimental results demonstrate that our method, referred to as CosDIA, yields a peptide identification count close to that of DIA-umpire. However, compared to DIA-umpire, we can establish the correspondence between original MS/MS spectra and pseudo-MS/MS spectra. Furthermore, compared to the CorrDIA method, our approach achieves higher efficiency in terms of time, reducing the time cost of the analysis process. These results highlight the potential advantages of the CosDIA method in DIA data analysis, providing a powerful tool and method for large-scale proteomics research.

Research on the Upper Limit of Accuracy for Predicting Theoretical Tandem Mass Spectrometry

In recent years, numerous theoretical tandem mass spectrometry prediction methods have been proposed, yet a systematic study and evaluation of their theoretical accuracy limits have not been conducted. If the accuracy of current methods approaches this limit, further exploration of new prediction techniques may become redundant. Conversely, a need for more precise prediction methods or models may be indicated. In this study, we have experimentally analyzed the limits of accuracy at different numbers of ions and parameters using repeated spectral pairs and integrating various similarity metrics. Results show significant achievements in accuracy for backbone ion methods with room for improvement. In contrast, full-spectrum prediction methods exhibit greater potential relative to the theoretical accuracy limit. Additionally, findings highlight the significant impact of normalized collision energy and instrument type on prediction accuracy, underscoring the importance of considering these factors in future theoretical tandem mass spectrometry predictions.